Abstract:
Fundamental statistical methods for analyzing competing risks data have been in
discussion for decades. However there’s still an uncertainty on how to approach this type
of data due to its complexities and thus there exist several gaps in the available
methodology particularly in the area of modeling and model validation. Hence, this has
become a topic of interest for many researchers. We review competing-risk regression
models with a view toward: testing for prognostic factors, testing for treatment effects
after adjusting for prognostic factors and model validation. . An example of prostate
cancer data from a French study is used to illustrate the methods examined. This includes
the application of the Lunn and McNeil regression model for testing prognostic factors
and treatments and the adaptation and modification of a goodness-of-fit test, suggested
in the literature, to test the hypothesis whether to include the covariates in a
multiplicative Cox proportional hazards model, against the hypothesis whether to include
the covariates in a more general class of additive-multiplicative model. Serum prostatic
acid phosphatase, Combined index of stage and histological grade, Size of primary
tumor, Serum hemoglobin level, Performance rating and age were identified as the more
vital factors for the survival of patients from death by prostate cancer. Furthermore, the
active treatments (estrogen) significantly effects time to death by prostate cancer, where
the survival experience of patients showed improvement for higher doses of estrogen
treatment. The goodness of fit test indicated that the model fit was adequate and that all
prognostic factors in the model had a multiplicative effect on hazard.
